If you are trying to find answers about Agile requirements gathering, this page may be one attempt of many. The truth is: doing requirements gathering the Agile way is not always straightforward. Why isn’t it simple? Mainly because: So where does that leave us? You’re probably not in a pure Agile environment, so you know the deal. The key is to get ideas for how to make this work, and apply what fits for your team.

When building or maintaining software, one thing is certain: data is everywhere. Whether you’re running an e-commerce platform, a banking system, or a social media network, your application is handling, storing, and processing a ton of data. This is where Volume Testing comes into play. Think of it as testing your application’s ability to stay calm and composed when it’s hit with an ocean of data – thousands of records, large files, and long lists of entries.

In the fast-paced world of software development, time-to-market is everything. With AI and generative AI tools making their mark, developers now have the power to reduce development time drastically while maintaining high code quality. One such game-changing application? AI-powered VS Code extensions for unit testing. These intelligent extensions are not just tools; they’re enablers, helping teams ship features faster, reduce bugs, and maintain confidence in their code.

Did you know 88% of users don’t return to sites with poor experiences, even if they offer excellent services and content? It’s time to recognize that a seamless user interface (UI) and user experience (UX) are essential to a product or company’s success, not mere luxuries. This is where Continuous UI Testing steps in. Ditching the conventional testing approaches, continuous UI testing integrates itself into almost every step of development & deployment.

Artificial intelligence (AI) is changing the software development landscape, particularly in the quality assurance (QA) domain. According to a market study, the AI market is projected to reach US$826.7 billion by 2030, driven by a CAGR of 28.46% and widely adopted across industries globally. Speed in today’s market cannot be overemphasized because the market demands faster and much better quality software.

Observability is a critical element of modern software development, unlocking awareness across complex and distributed systems with ease. This has allowed developers to monitor, understand, and debug their applications effectively, leveraging existing resources for more efficient lifecycle management and iteration. In the context of Python, observability is an engine for boosting and maintaining the performance, reliability, and stability of the implementation.

Businesses send and receive several invoices and payment receipts in digital formats, such as scanned PDFs, text documents, or Excel files. While digital formats have allowed workplaces to transition to a paperless environment, they have introduced a new challenge for business analysts: extracting the data from invoices and using it to draw relevant insights.

If you’re an SDET who’s been using Playwright for API testing, you might be all too familiar with the frustrations of dealing with database dependencies, data management, and the endless need for cleanup. Let’s face it – Playwright, while fantastic for UI testing, can be cumbersome when it comes to API testing. But what if there’s a better way to handle this?

Performance tests, end-to-end tests, integration tests. There are many different types of tests you can run on your infrastructure. One of the most overlooked kinds is load testing. Failure to include load tests in your supply chain can be detrimental, as you will be missing out on a number of benefits. Some of the big advantages of load testing Kubernetes are.

In a significant stride toward transforming AI infrastructure, ClearML has recently announced a collaboration with AMD. By integrating with AMD’s powerful hardware and open-source ROCm software with ClearML’s silicon-agnostic, end-to-end platform, we’re empowering IT teams and AI builders to innovate with ease across diverse infrastructures and integrate GPUs from multiple vendors.